Scale Invariant Feature Transform (SIFT) may be utilized to detect and extract local feature descriptors that may be invariant to changes in, e.g., illumination, image noise, rotation, scaling, viewpoint. For example, SIFT may be applied to computer vision problems, including, e.g., object or scene recognition, face recognition, object detection, images match, 3-dimension structure construction, stereo correspondence, and motion tracking. SIFT may be a time-consuming task. Also, there are some scenarios (e.g. online object recognition) that may require SIFT features to be extracted and matched in real-time or even in super-real-time. SIFT feature extraction may be implemented or accelerated on graphics processing unit. However, SIFT algorithm is serial and designed for single processor system. Straightforward parallelization of the algorithm may deteriorate scaling performance due to load imbalance.